AI0-001 AI Security, Ethics and Governance Practice Question
Which TWO are common attack vectors against AI systems? (Choose two.)
⚠ Common exam trap
The AI0-001 exam often tests the distinction between traditional cybersecurity attacks (SQL injection, XSS, buffer overflow) and AI-specific threats (data poisoning, adversarial examples), so the trap is that candidates mistakenly apply general security knowledge to AI systems without recognizing the unique attack surfaces.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data poisoning
Data poisoning (D) is a correct answer because it is a canonical AI-specific attack vector: adversaries corrupt or inject malicious samples into the training data so the model learns skewed decision boundaries, degrading accuracy or implanting backdoors that trigger on specific inputs. Adversarial examples (E) are also correct because they are deliberately perturbed inputs (often imperceptible changes, e.g., small Lp-norm perturbations) crafted to cause misclassification or evasion at inference time, exploiting the model's learned gradients. By contrast, SQL injection (A) targets database query construction in web applications, cross-site scripting (B) injects client-side script into web pages to attack users' browsers, and buffer overflow (C) exploits memory-safety flaws in native code — all are classic application/software vulnerabilities, not attack vectors specific to AI systems.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SQL injection
Why it's wrong here
SQL injection targets database query construction, which AI systems do not inherently expose. It is tempting because it is a well-known injection attack, and would be correct when testing web applications that build SQL statements from untrusted input.
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Cross-site scripting
Why it's wrong here
Cross-site scripting exploits browser rendering of untrusted content, not AI model behaviour. It is tempting because it is a common web attack vector, and would be correct when assessing applications that reflect user input into HTML pages.
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Buffer overflow
Why it's wrong here
Buffer overflow exploits memory handling in native software, not the model, prompt or training pipeline. It is tempting because it is a classic vulnerability class, and would be correct when assessing conventional compiled applications rather than AI-specific attack surfaces.
- ✓
Data poisoning
Why this is correct
Data poisoning corrupts the training corpus, causing the model to learn manipulated patterns or backdoors. It targets the learning pipeline itself rather than inference, which is why it is a distinct attack vector against AI systems alongside adversarial inputs and prompt injection.
- ✓
Adversarial examples
Why this is correct
Adversarial examples are inputs deliberately perturbed to cause misclassification or incorrect output at inference time. They exploit the model's learned decision boundaries rather than the training data, making them a distinct attack vector from data poisoning.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.